The present invention relates to detection of plants and, in particular, to optical detection of plants which are planted in a field, in a greenhouse or on farmland, or which exist in any other way.
Detection of plants is important in agricultural engineering, so called phenotyping of plants having to be mentioned here. A further example of detection consists in identifying plants in order to enable, e.g., automatic pulling out of unwanted plants, i.e. weeds.
For three-dimensional detection of objects, various methods are commonly used, such as stripe-light methods or light section methods. Said methods offer high spatial three-dimensional resolution. However, with regard to illumination, they depend on defined ambient conditions. A further disadvantage is that three-dimensional detection cannot be effected within a very short time period.
With stripe-light methods, different light patterns must be successively projected onto the object, whereas with light section methods, only one contour line is detected at a given point in time. Thus, for three-dimensional detection, the object must be scanned.
In order to produce the defined light conditions on farmland and/or in a field, one may set up a tent which keeps the ambient light from the area to be detected. Subsequently, a defined ambient condition may be produced within said lightproof tent so as to employ the light section method or the stripe-light method. Once a specific area located within the tent has been dealt with, the tent must be taken down and be set up again at another location before the light section method and/or the stripe-light method may again be employed at said other location.
This approach is time-consuming and therefore expensive. In addition, it is not suited for three-dimensional detection of relatively large areas since this procedure is too slow. To achieve sufficient throughput, a large number of teams would have to work in parallel, which necessitates many tents, many light section cameras and, thus, also a large requirement in terms of trained specialists, all of which leads to an increase in cost.
On the other hand, particularly in the development of plant seeds it is very important to obtain an objective evaluation of the seedlings produced from a certain type of seed at regular intervals, such as every week to every two weeks, without said seedlings being destroyed. It shall be noted that as test fields, fields are to be employed which have a minimum size in order to have reasonably realistic growth conditions. Therefore, relatively large test areas will be necessitated if large cultivation areas for a type of seed are intended.
In addition to sizable test areas, accurate data on spatial orientation of plant leaves, on the size of the plant leaves, on the structure of the plant leaves, etc. are necessitated in order to obtain accurate information about a specific type of seed. In order to reliably obtain said information when the plants must not be pulled out, three-dimensional detection is necessitated since in the event of two-dimensional detection only projections and/or silhouettes of leaves are detected, their orientations cannot be determined, and their true surface areas also cannot be determined since one cannot draw any conclusions as to the area itself from a mere projection without knowledge of the orientation of the projected area.
Extraction of plant features from measurement data of imaging methods is necessitated, therefore, in various applications of modern agricultural engineering and agriculture sciences. In this context it is necessitated to identify the plant in the captured data and to distinguish between image regions which are part of the plant and image regions which are not part of the plant. For segmentation, color pictures of a plant are typically used for segmentation since in said color pictures, the green plant may be clearly distinguished from, e.g. brown soil.
A standard method of separating plant and background areas with the aid of preliminary data is described in the specialist publication “Improving Plant Discrimination in image processing by use of different colour space transformation”, I. Philipp, T. Rath, Computers and Electronics in Agriculture 35 (2002) 1-15 (Elsevier).
Here, the RGB color channels of each individual pixel are suitably transformed, and subsequently, a decision is made by means of a decision criterion as to whether the pixel is classified as a plant image point or non-plant image point (background). For example, the proportion of the green channel in the overall color may be determined for each pixel and may be classified as a plant pixel if said proportion exceeds a threshold value.
What is problematic in said methods is the small amount of information of a color picture. There are only three values available for each image point: the levels of brightness of the green channel, of the red channel and of the blue channel. However, especially in the detection of plants, there may be large differences in brightness, for example. Reasons for this are, e.g., different angles of the plant leaves in relation to the light source and shadows cast by parts of plants. In addition, the levels of brightness within a leaf or between several leaves are not mutually homogenous. Leaves frequently have a light primary vein or lighter stalks.
Due to the large variability that is possible and to the limited dynamics of color cameras it happens that light plant regions are overexposed, and that dark plant regions are underexposed. For example, light leaf stalks are overexposed, whereas some regions at the leaf edges are too dark for reliable segmentation due to their downward curvature.